I’m releasing malloc (MultiplicativeConstraint), a production-grade, open-source library for solving large, constraint-rich partitioning problems—think resource allocation, scheduling, clustering, supply chain, EDA, and HPC.
Why is this interesting?
Physics-inspired math: Uses spectral theory and heat-kernel action minimization—correlation with target objectives is ρ ≈ 0.996.
Enterprise scalability: Sparse matrix core solves 100,000-node problems in under 90 seconds with only 23MB memory.
Adaptable constraints: Neural module adapts to real-world, contradictory constraints with up to 83% satisfaction.
Key applications: Cloud resource allocation, HPC load balancing, clustering, logic optimization, graph partitioning, bin packing, and more.
Limitations: Not a general P=NP solution; works best when structural regularity exists.
Replicable results: All benchmarks, proofs (MATH.md), and performance demos are open in the repo.
Try it out:
Repo + docs: https://codeberg.org/aninokuma/malloc
README covers the API, benchmarks, and supported problems.
Comments URL: https://news.ycombinator.com/item?id=45568451
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Source: codeberg.org
